摘要
统计回归模型是土石坝渗流安全监测分析中最常用和相对成熟的数学模型,通过分离变量分析环境量对效应量的影响特性是进行土石坝渗流安全评价的重要途径之一。针对采用最小二乘法分离土石坝渗流统计回归模型各影响分量时存在的因子多重共线性问题,提出了基于主成分分析的变量分离方法,并将其应用于冶勒混凝土心墙堆石坝的变量分析中。实例应用结果表明,该方法能有效避免因模型因子间的多重共线性而造成的回归系数不合理和分量峰值错位的问题,提高了分量分离结果的力学规律一致性与结构特性变化的吻合度。
The statistical regression model is the most commonly used and relatively mature mathematical model for seepage safety monitoring analysis of earth rockfill dam. Analyzing the influence of environmental factors on the effect variables with separating variables is one of the important ways to evaluate the seepage safety of earth rockfill dam. In view of the factor multi-collinearity problem when separating the influence component of earth rockfill dam seepage re- gression model by the least square method, the variable separation method based principal component analysis was put forward to analyze the Yele concrete core wall rockfill dam. Engineering application shows that the method can effectively avoid unreasonable regression coefficient and peak misalignment caused by multi-collinearity among the model factors, which improves mechanics consistency and fitness with changes in structural properties of the variable separation results.
出处
《水电能源科学》
北大核心
2018年第1期79-81,108,共4页
Water Resources and Power
关键词
土石坝渗流
统计回归模型
变量分离方法
多重共线性
主成分分析
earth rockfill dam seepage
statistical regression model
variable separation method
multi-collinearity
principal component analysis
